Multi-hop question answering aims to aggregate multiple pieces of documents to model the multi-hop reasoning chain and predict the answer [1]}, [2]}, [3]}, [4]}.
Prior methods
mainly focus on utilizing a single reader to model the interaction between the question and relevant context, and simultaneously or separately predict the supporting sentences and answer within one step. [5]} and [6]} propose to construct graphs based on entity information from scattered paragraphs and utilize graph neural networks as the reader to reason out the answer. Then HDE-Graph [7]}, HGN [8]} and SAE [9]} enrich information in the entity graph by extending nodes of other granularity to build a hierarchical graph and improve the interaction between the question and context. Some other methods [10]}, [11]}, [12]}, [13]} adopt pre-trained models as the powerful reader for multi-hop reasoning and achieve promising results.
However, these one-step reader methods directly encode the relevant context and question for answer prediction while neglecting to illustrate the explicit reasoning process.
